Abstract
This paper focuses on the study of the evolutionary mechanism governing the temperature field of geothermal reservoir under low-temperature tailwater reinjection conditions, which is crucial for the sustainable geothermal energy management. With advancing exploitation of geothermal resources deepens, precise understanding of this mechanism becomes paramount for devising effective reinjection strategies, optimizing reservoir utilization, and bolstering the economic viability of geothermal energy development. The article presents a comprehensive review of temperature field evolution across diverse heterogeneous thermal reservoirs under low-temperature tailwater reinjection conditions, and analyzes key factors influencing this evolution. It evaluates existing research methods, highlighting their strengths and limitations. The study identifies gaps in the application of rock seepage and heat transfer theories on a large scale, alongside the need for enhanced accuracy in field test results, particularly regarding computational efficiency of fractured thermal reservoir models under multi-well reinjection conditions. To address these shortcomings, the study proposes conducting large-scale rock seepage and heat transfer experiments, coupled with multi-tracer techniques for field testing, aimed at optimizing fractured thermal reservoir models' computational efficiency under multi-well reinjection conditions. Additionally, it suggests integrating deep learning methods into research endeavors. These initiatives are of significance in deepening the understanding of the evolution process of the temperature field in deep thermal reservoirs and enhancing the sustainability of deep geothermal resource development.
